Information Visualization in the Age of AI:
Bridging Data, Design, and Intelligence

By: Sougata Mukherjea
(Author, Information Visualization)

The exponential growth of data has made information visualization a core discipline for data science, artificial intelligence, analytics, and decision-making. From business dashboards and scientific discovery to machine learning model interpretation, visualization is no longer an optional add-on—it is a fundamental tool for understanding complex information. Despite this importance, many existing resources on visualization either focus narrowly on perceptual theory, emphasizing isolated visualization techniques, or fail to address the realities of modern AI-driven systems.

This book, Information Visualization, is written to address this gap. It provides a comprehensive, end-to-end treatment of visualization that connects foundational principles with modern applications, including visual analytics for deep learning and the use of AI to automate and enhance visualization itself. The book is designed for students, researchers, and practitioners who want not only to create visually appealing charts but also to develop meaningful, interactive, and intelligent visualizations for real-world problems.

Why a New Book on Information Visualization?

Classic visualization textbooks have laid strong foundations in perception, visual encoding, and design principles. However, the visualization landscape has evolved significantly in recent years due to three major shifts:

• The rise of complex data types such as graphs, trees, text collections, and high-dimensional datasets
• The central role of interaction in exploratory data analysis and sensemaking
• The convergence of visualization and AI, particularly in explainable artificial intelligence (XAI), visual analytics, and utilizing AI techniques for generating and understanding visualization.

Many existing books treat these topics either superficially or in isolation. There is a lack of a single, coherent resource that integrates classical information visualization with modern AI-centric workflows. This book fills that gap by systematically connecting data, visual representation, interaction, implementation, and intelligent analytics.

Foundations: Data, Visual Encoding, and Perception

The book begins by establishing strong conceptual foundations. Early chapters introduce readers to the aims behind information visualization, key challenges in visual representation, and the role visualization plays in amplifying human cognition.

A dedicated chapter on data discusses attribute types, dataset structures, and the implications of data characteristics for visualization design. This is followed by an in-depth treatment of visual encoding, explaining how visual variables such as position, color, shape, and size are used to represent data effectively. The discussion is grounded in perceptual principles and Gestalt laws of pattern perception, enabling readers to understand why some visualizations work better than others.

These foundational chapters ensure that readers do not treat visualization as a collection of ad-hoc techniques, but as a principled design discipline.

Visualizing Common Data Representations

The core of the book focuses on visualizing widely encountered data types and analytical scenarios. Separate chapters are devoted to:

• Tabular data, with techniques organized by analytical tasks such as comparison, composition, correlation, and distribution.
• Multidimensional data, including visualization methods for large tables and an introduction to dimensionality reduction techniques.
• Time-oriented data, covering time-series visualization and applications where time plays a critical role, such as project management and IT systems.
• Hierarchical data, with detailed discussions of tree visualization using node-link and space-filling approaches.
• Graphs and networks, addressing layout challenges and graph drawing algorithms.
• Textual data, including visualizations for documents, document collections, and search results.

Unlike many texts that emphasize static charts, this book devotes significant attention to interaction techniques. Chapters on interaction and graph/tree interactions introduce methods such as dynamic queries, focus+context techniques, and hyperbolic geometry, highlighting how interaction enables scalable exploration of large and complex datasets.

From Theory to Practice: Tools and Design Principles

To bridge the gap between theory and real-world deployment, the book includes chapters on visualization tools and libraries. Readers are introduced to popular visualization frameworks and programming libraries in JavaScript and Python, enabling them to translate concepts into working systems.

A dedicated chapter on design principles focuses on graphical excellence, effective visual communication, and common design considerations. This practical orientation makes the book suitable not only for academic courses but also for industry practitioners who design dashboards, analytical tools, and decision-support systems.

Visualization Meets Artificial Intelligence

One of the defining contributions of this book is its focus on the intersection of visualization and AI.

A full chapter on visual analytics for deep learning examines how visualization techniques are used to understand, debug, and explain complex models. The chapter covers a wide range of architectures, including multilayer perceptrons, convolutional neural networks, generative adversarial networks, transformers, and graph neural networks. This makes the book particularly relevant for researchers and practitioners working in explainable AI and trustworthy machine learning.

Complementing this, the final chapter explores using AI for visualization, discussing how machine learning and AI techniques can assist in visualization generation, visualization understanding (such as question answering over charts), and graph layout optimization. This forward-looking perspective reflects emerging trends where AI acts as a collaborator in the visualization process.

Who Is This Book For?

This book is suitable for:

• Graduate and advanced undergraduate students in computer science, data science, AI, and HCI
• Researchers entering the fields of information visualization, visual analytics, or explainable AI
• Industry professionals building analytical systems, dashboards, and AI-driven applications

By combining classical visualization principles with modern AI-centric applications, the book serves as both a textbook and a reference for contemporary visualization practice.

Filling the Gap

Information Visualization fills a critical gap in the current literature by unifying foundational visualization theory, interaction techniques, practical implementation, and AI-driven visual analytics within a single coherent framework. In doing so, it reflects how visualization is actually used today—not just to display data, but to reason about complex systems, models, and decisions in an AI-driven world.